Use of Nomograms for Personalized Decision-Analytic Recommendations

Risk nomogram for papillary thyroid microcarcinoma with central lymph node metastasis and postoperative thyroid function follow-up

Background

The treatment for papillary thyroid microcarcinoma (PTMC) is controversial. Central lymph node metastasis (CLNM) is one of the main predictors of recurrence and survival, accurate preoperative identification of CLNM is essential for surgical protocol establishment for PTMC. The objective of this study was to establish a nomogram to predict the possibility of CLNM in PTMC patients.

Methods

A total of 3023 PTMC patients were randomly divided into two groups by a ratio of 7 to 3, the training group (n = 2116) and validation group (n = 907). The LASSO regression model and multivariate logistic regression analysis were performed to examine risk factors associated with CLNM. A nomogram for predicting CLNM was established and internally validated. Meanwhile, we follow-up the serum thyroid function FT3, FT4, TSH, Tg, TGAb and TPOAb in 789 PTMC patients for 4 years after surgery and compared the differences between the CLNM (+) and CLNM (-) groups, respectively.

Results

The LASSO regression model and multivariate logistic regression analysis showed that younger age, lower BMI, being male, location in the lower pole, calcification, 1 ≥ diameter ≥ 0.5 cm, multifocality lesions, extra thyroidal extension (ETE), enlargement of central lymph node (ECLN), lateral lymph node metastasis (LLNM) and higher carcinoembryonic antigen were the ultimate risk factors for determining CLNM. A nomogram for predicting CLNM was constructed based on the influencing factors and internally validated. By establishing the prediction model, the AUC of CLNM in the training and validation groups were 0.73 (95% CI, 0.70-0.76) and 0.75 (95% CI, 0.71-0.79) respectively. Results of the DCA showed that the model is clinically useful when deciding on intervention in the most range of the threshold probability. A 4-year follow-up of thyroid function showed that FT3 and FT4 remained at stable levels after 3 months postoperative and were higher in the CLNM (+) group than in the CLNM (-) group. Hypothyroidism appeared predominantly within 3 months after surgery. The overall incidence of the CLNM (+) group and CLNM (-) groups were 16.46% and 12.04%, respectively.

Conclusion

The nomogram model constructed in this study has a good predictive effect on CLNM in PTMC patients and provides a reasonable reference for clinical treatment.

Development and validation of a nomogram for predicting mechanical ventilation need among acutely intoxicated patients with impaired consciousness

BACKGROUND

A considerable portion of acutely intoxicated patients is presented with impaired consciousness. Early identification of those patients who require advanced medical care, such as mechanical ventilation (MV), can improve their prognosis.

METHODS

This study included 330 acutely intoxicated patients who were presented with impaired consciousness and admitted to Tanta University Poison Control Center, Egypt, in the period from January 2021 to December 2023. Patients were enrolled in derivation (257 patients) and validation (73 patients) cohorts. Patients' data were analyzed to develop and validate a predictive nomogram to determine the probability of MV need in acutely intoxicated patients.

RESULTS

Significant predictors for MV need were mean arterial blood pressure (OR = 0.96, p = .014), PaO2 (OR = 0.96, p = .001), pH (OR = 0.00, p < . 001), and glucose/potassium ratio (OR = 1.59, p = .030). These four parameters were used to formulate a bedside nomogram. Receiver-operating characteristic (ROC) analysis for the proposed nomogram shows that area under the curve (AUC) = 95.7%, accuracy = 93.4%, sensitivity = 88.9%, and specificity = 95.1%. The internal validation for the developed nomogram was assessed using a bootstrapping method and calibration curve. Regarding external validation, AUCs for the developed nomogram probability was 96.5%, and for predicted probability using the developed nomogram was 97.8%.

CONCLUSION

The current study provides a validated nomogram that could be used as a reliable tool for the accurate prediction of MV need among acutely intoxicated patients with impaired consciousness. It could assist in the early identification of patients who will require MV, especially in low-income countries with limited resources.

Prognostic factors in acute poisoning with central nervous system xenobiotics: development of a nomogram predicting risk of intensive care unit admission

Background

Acute intoxication with central nervous system (CNS) xenobiotics is an increasing global problem. Predicting the prognosis of acute toxic exposure among patients can significantly alter the morbidity and mortality. The present study outlined the early risk predictors among patients diagnosed with acute exposure to CNS xenobiotics and endorsed bedside nomograms for identifying patients requiring intensive care unit (ICU) admission and those at risk of poor prognosis or death.

Methods

This study is a 6-year retrospective cohort study conducted among patients presented with acute exposure to CNS xenobiotics.

Results

A total of 143 patients' records were included, where (36.4%) were admitted to the ICU, and a significant proportion of which was due to exposure to alcohols, sedative hypnotics, psychotropic, and antidepressants (P = 0.021). ICU admission was associated with significantly lower blood pressure, pH, and HCO₃ levels and higher random blood glucose (RBG), serum urea, and creatinine levels (P < 0.05). The study findings indicate that the decision of ICU admission could be determined using a nomogram combining the initial HCO₃ level, blood pH, modified PSS, and GCS. HCO₃ level < 17.1 mEq/L, pH < 7.2, moderate-to-severe PSS, and GCS < 11 significantly predicted ICU admission. Moreover, high PSS and low HCO₃ levels significantly predicted poor prognosis and mortality. Hyperglycemia was another significant predictor of mortality. Combining initial GCS, RBG level, and HCO₃ is substantially helpful in predicting the need for ICU admission in acute alcohol intoxication.

Conclusion

The proposed nomograms yielded significant straightforward and reliable prognostic outcomes predictors in acute exposure to CNS xenobiotics.

Children's Hip Predictive (CHiP) Score: A Triage Tool for Hip Dislocation in Children Referred With Suspected Hip Dysplasia

BACKGROUND

A fundamental tenent of treating developmental dysplasia of the hip is to identify patients with dislocated hips early so as to avoid the long-term sequelae of late diagnosis. The aim of this study was to develop a readily useable triage tool for patients with suspected hip dislocation, based on the clinical history and examination findings of the referring practitioner.

METHODS

All primary care referrals (n=934) over a 3-year period for suspected developmental dysplasia of the hip to a tertiary pediatric center were evaluated. Defined parameters with respect to history and clinical examination were evaluated. Multivariable logistic regression was used to establish predictors of hip dislocation, and from this a predictive model was derived which incorporated significant predictors of dislocation. An illustrative nomogram translated this predictive model into a usable numerical scoring system called the Children's Hip Prediction score, which estimates probability of hip dislocation.

RESULTS

There were 97 dislocated hips in 85 patients. The final predictive model included age, sex, family history, breech, gait concerns, decreased abduction, leg length discrepancy, and medical/neurological syndrome. The area under receiver operating curve for the model is 0.761. A Children's Hip Prediction score of≥5 corresponds to a sensitivity of 76.3% and a score of≥15 has a specificity of 97.8%, corresponding to an odds ratio of 27.3 for increased risk of dislocation.

CONCLUSION

We found that a novel clinical prediction score, based on readily available history and examination parameters strongly predicted risk of dislocations in hip dysplasia referral. It is hoped that this tool could be utilized to optimize resource allocation and may be of particular benefit in less well-resourced health care systems.

LEVEL OF EVIDENCE

Level II.

Development of a risk prediction nomogram for disposition of acute toxic exposure patients to intensive care unit

Early risk stratification of acutely poisoned patients is essential to identify patients at high risk of intensive care unit (ICU) admission. We aimed to develop a prognostic model and risk-stratification nomogram based on the readily accessible clinical and laboratory predictors on admission for the probability of ICU admission in acutely poisoned patients. This retrospective cohort study included adult patients with acute toxic exposure to a drug or a chemical substance. Patients' demographic, toxicologic, clinical and laboratory data were collected. Among the 1260 eligible patients, 180 (14.3%) were admitted to the ICU. We developed a generalized prognostic model for predicting ICU admission in patients with acute poisoning. The predictors included the Glasgow coma scale, oxygen saturation, diastolic blood pressure, respiratory rate and blood bicarbonate concentration. The model displayed excellent discrimination and calibration (optimistic-adjusted area under the curve = 0.924 and optimistic-adjusted Hosmer and Lemeshow test = 0.922, respectively) when internally validated. Additionally, we developed prognostic models that determine ICU admission in patients with specific poisonings. Furthermore, we constructed risk-stratification nomograms that rank the probability of ICU admission in these patients. The developed risk-stratification nomograms help decision-making regarding ICU admission in acute poisonings. Future external validation in independent cohorts is necessary before clinical application.

Two effective clinical prediction models to screen for obstructive sleep apnoea based on body mass index and other parameters

BACKGROUND AND OBJECTIVE

The diagnosis of obstructive sleep apnea (OSA) relies on polysomnography which is time-consuming and expensive. We therefore aimed to develop two simple, non-invasive models to screen adults for OSA.

METHODS

The effectiveness of using body mass index (BMI) and a new visual prediction model to screen for OSA was evaluated using a development set (1769 participants) and confirmed using an independent validation set (642 participants).

RESULTS

Based on the development set, the best BMI cut-off value for diagnosing OSA was 26.45 kg/m², with an area under the curve (AUC) of 0.7213 (95% confidence interval (CI), 0.6861-0.7566), a sensitivity of 57% and a specificity of 78%. Through forward conditional logistic regression analysis using a stepwise selection model developed from observed data, seven clinical variables were evaluated as independent predictors of OSA: age, BMI, sex, Epworth Sleepiness Scale score, witnessed apnoeas, dry mouth and arrhythmias. With this new model, the AUC was 0.7991 (95% CI, 0.7668-0.8314) for diagnosing OSA (sensitivity, 75%; specificity, 71%). The results were confirmed using the validation set. A nomogram for predicting OSA was generated based on this new model using statistical software.

CONCLUSIONS

BMI can be used as an indicator to screen for OSA in the community. We created an internally validated, highly distinguishable, visual and parsimonious prediction model comprising BMI and other parameters that can be used to identify patients with OSA among outpatients. Use of this prediction model may help to improve clinical decision-making.

Dynamic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

OBJECTIVE

To establish a dynamic nomogram based on preoperative clinical data for prediction of lateral lymph node metastasis (LLNM) of papillary thyroid carcinoma.

STUDY DESIGN

Retrospective study.

SETTING

The Sixth Affiliated Hospital of Sun Yat-Sen University.

METHODS

The data of 477 patients from 2 centers formed the training group and validation group and were retrospectively reviewed. Preoperative clinical factors influencing LLNM were identified by univariable and multivariable analysis and were to construct a predictive dynamic nomogram for LLNM. Receiver operating characteristic analysis and calibration curves were used to evaluate the predictive power of the nomogram.

RESULTS

The following were identified as independent risk factors for LLNM: male sex (odds ratio [OR] = 4.6, P = .04), tumor size ≥10.5 mm (OR = 7.9, P = .008), thyroid nodules (OR = 6.1, P = .013), irregular tumor shape (OR = 24.6, P = .001), rich lymph node vascularity (OR = 9.7, P = .004), and lymph node location. The dynamic nomogram constructed with these factors is available at https://zxh1119.shinyapps.io/DynNomapp/. The nomogram showed good performance, with an area under the curve of 0.956 (95% CI, 0.925-0.986), a sensitivity of 0.87, and a specificity of 0.91, if high-risk patients were defined as those with a predicted probability ≥0.3 or total score ≥200. The nomogram performed well in the external validation cohort (area under the curve, 0.915; 95% CI, 0.862-0.967).

CONCLUSIONS

The dynamic nomogram for preoperative prediction of LLNM in papillary thyroid carcinoma can help surgeons identify high-risk patients and develop individualized treatment plans.

Predicting urine output after kidney transplantation: development and internal validation of a nomogram for clinical use

Purpose:

To analyze pre-transplantation and early postoperative factors affecting post-transplantation urine output and develop a predictive nomogram.

Patients and Methods:

Retrospective analysis of non-preemptive first transplanted adult patients between 2001-2016. The outcomes were hourly diuresis in mL/Kg in the 1^st (UO₁) and 8^th (UO₈) postoperative days (POD). Predictors for both UO₁ and UO₈ were cold ischemia time (CIT), patient and donor age and sex, HLA I and II compatibility, pre-transplantation duration of renal replacement therapy (RRT), cause of ESRD (ESRD) and immunosuppressive regimen. UO₈ predictors also included UO₁, 1^st/0^th POD plasma creatinine concentration ratio (Cr_1/0), and occurrence of acute cellular rejection (AR). Multivariable linear regression was employed to produce nomograms for UO₁ and UO₈.

Results:

Four hundred and seventy-three patients were included, mostly deceased donor kidneys’ recipients (361, 70.4%). CIT inversely correlated with UO₁ and UO₈ (Spearman's p=-0.43 and −0.37). CR_1/0 inversely correlated with UO₈ (p=-0.47). On multivariable analysis UO₁ was mainly influenced by CIT, with additional influences of donor age and sex, HLA II matching and ESRD. UO₁ was the strongest predictor of UO₈, with significant influences of AR and ESRD.

Conclusions:

The predominant influence of CIT on UO₁ rapidly wanes and is replaced by indicators of functional recovery (mainly UO₁) and allograft's immunologic acceptance (AR absence). Mean absolute errors for nomograms were 0.08 mL/Kg h (UO₁) and 0.05 mL/Kg h (UO₈).

Predicting Outcomes in Emergency Medical Admissions Using a Laboratory Only Nomogram

Background

We describe a nomogram to explain an Acute Illness Severity model, derived from emergency room triage and admission laboratory data, to predict 30-day in-hospital survival following an emergency medical admission.

Methods

For emergency medical admissions (96,305 episodes in 50,612 patients) between 2002 and 2016, the relationship between 30-day in-hospital mortality and admission laboratory data was determined using logistic regression. The previously validated Acute Illness Severity model was then transposed to a Kattan-style nomogram with a Stata user-written program.

Results

The Acute Illness Severity was based on the admission Manchester triage category and biochemical laboratory score; these latter were based on the serum albumin, sodium, potassium, urea, red cell distribution width, and troponin status. The laboratory admission data was predictive with an AUROC of 0.85 (95% CI: 0.85, 0.86). The sensitivity was 94.4%, with a specificity of 62.7%. The positive predictive value was 21.2%, with a negative predictive value of 99.1%. For the Kattan-style nomogram, the regression coefficients are converted to a 100-point scale with the predictor parameters mapped to a probability axis. The nomogram would be an easy-to-use tool at the bedside and for educational purposes, illustrating the relative importance of the contribution of each predictor to the overall score.

Conclusion

A nomogram to illustrate and explain the prognostic factors underlying an Acute Illness Severity Score system is described.

A Nomogram for Predicting the Likelihood of Obstructive Sleep Apnea to Reduce the Unnecessary Polysomnography Examinations

Background:

The currently available polysomnography (PSG) equipments and operating personnel are facing increasing pressure, such situation may result in the problem that a large number of obstructive sleep apnea (OSA) patients cannot receive timely diagnosis and treatment, we sought to develop a nomogram quantifying the risk of OSA for a better decision of using PSG, based on the clinical syndromes and the demographic and anthropometric characteristics.

Methods:

The nomogram was constructed through an ordinal logistic regression procedure. Predictive accuracy and performance characteristics were assessed with the area under the curve (AUC) of the receiver operating characteristics and calibration plots, respectively. Decision curve analyses were applied to assess the net benefit of the nomogram.

Results:

Among the 401 patients, 73 (18.2%) were diagnosed and grouped as the none OSA (apnea-hypopnea index [AHI] <5), 67 (16.7%) the mild OSA (5 ≤ AHI < 15), 82 (20.4%) the moderate OSA (15 ≤ AHI < 30), and 179 (44.6%) the severe OSA (AHI ≥ 30). The multivariable analysis suggested the significant factors were duration of disease, smoking status, difficulty of falling asleep, lack of energy, and waist circumference. A nomogram was created for the prediction of OSA using these clinical parameters and was internally validated using bootstrapping method. The discrimination accuracies of the nomogram for any OSA, moderate-severe OSA, and severe OSA were 83.8%, 79.9%, and 80.5%, respectively, which indicated good calibration. Decision curve analysis showed that using nomogram could reduce the unnecessary polysomnography (PSG) by 10% without increasing the false negatives.

Conclusions:

The established clinical nomogram provides high accuracy in predicting the individual risk of OSA. This tool may help physicians better make decisions on PSG arrangement for the patients referred to sleep centers.

Prostate cancer nomograms: a review of their use in cancer detection and treatment

As prostate cancer treatment discussions have grown more complex, increasing numbers of nomograms to guide decision-making have been found in the literature. Such nomograms can influence every step in the prostate cancer therapeutic process, from determining the need for biopsy to the need for adjuvant therapy. With a properly counseled patient who is aware of the limitations of nomograms, such tools assist in the shared decision-making that characterizes modern informed consent.

Baseline characteristics of patients with nerve-related neck and arm pain predict the likely response to neural tissue management

STUDY DESIGN

Planned secondary analysis of a randomized controlled trial comparing neural tissue management (NTM) to advice to remain active.

OBJECTIVE

To develop a model that predicts the likelihood of patient-reported improvement following NTM.

BACKGROUND

Matching patients to an intervention they are likely to benefit from potentially improves outcomes. However, baseline characteristics that predict patients' responses to NTM are unknown.

METHODS

Data came from 60 consecutive adults who had nontraumatic, nerve-related neck and unilateral arm pain for at least 4 weeks. Participants were assigned to a group that received NTM (n = 40), which involved brief education, manual therapy, and nerve gliding exercises for 4 treatments over 2 weeks, or to a group that was given advice to remain active (n = 20), which involved instruction to continue their usual activities. The participants' global rating of change at a 3- to 4-week follow-up defined improvement. Penalized regression of NTM data identified the best prediction model. A medical nomogram was created for prediction model scoring. Post hoc analysis determined whether the model predicted a specific response to NTM.

RESULTS

Absence of neuropathic pain qualities, older age, and smaller deficits in median nerve neurodynamic test range of motion predicted improvement. Prediction model cutoffs increased the likelihood of improvement from 53% to 90% (95% confidence interval: 56%, 98%) or decreased the likelihood of improvement to 9% (95% confidence interval: 1%, 42%). The model did not predict the outcomes of the advice to remain active group.

CONCLUSION

Baseline characteristics of patients with nerve-related neck and arm pain predicted the likelihood of improvement with NTM. Model performance needs to be validated in a new sample using different comparison interventions and longer follow-up. Australian New Zealand Clinical Trials Registry (ACTRN 12610000446066).

LEVEL OF EVIDENCE

Prognosis, level 2b-.

Prediction tools in surgical oncology

Artificial neural networks, prediction tables, and clinical nomograms allow physicians to transmit an immense amount of prognostic information in a format that exhibits comprehensibility and brevity. Current models demonstrate the feasibility to accurately predict many oncologic outcomes, including pathologic stage, recurrence-free survival, and response to adjuvant therapy. Although emphasis should be placed on the independent validation of existing prediction tools, there is a paucity of models in the literature that focus on quality of life outcomes. The unification of tools that predict oncologic and quality of life outcomes into a comparative effectiveness table will furnish patients with cancer with the information they need to make a highly informed and individualized treatment decision.

Is it time to tailor the prediction of radio-induced toxicity in prostate cancer patients? Building the first set of nomograms for late rectal syndrome

PURPOSE

Development of user-friendly tools for the prediction of single-patient probability of late rectal toxicity after conformal radiotherapy for prostate cancer.

METHODS AND MATERIALS

This multicenter protocol was characterized by the prospective evaluation of rectal toxicity through self-assessed questionnaires (minimum follow-up, 36 months) by 718 adult men in the AIROPROS 0102 trial. Doses were between 70 and 80 Gy. Nomograms were created based on multivariable logistic regression analysis. Three endpoints were considered: G2 to G3 late rectal bleeding (52/718 events), G3 late rectal bleeding (24/718 events), and G2 to G3 late fecal incontinence (LINC, 19/718 events).

RESULTS

Inputs for the nomogram for G2 to G3 late rectal bleeding estimation were as follows: presence of abdominal surgery before RT, percentage volume of rectum receiving >75 Gy (V75Gy), and nomogram-based estimation of the probability of G2 to G3 acute gastrointestinal toxicity (continuous variable, which was estimated using a previously published nomogram). G3 late rectal bleeding estimation was based on abdominal surgery before RT, V75Gy, and NOMACU. Prediction of G2 to G3 late fecal incontinence was based on abdominal surgery before RT, presence of hemorrhoids, use of antihypertensive medications (protective factor), and percentage volume of rectum receiving >40 Gy.

CONCLUSIONS

We developed and internally validated the first set of nomograms available in the literature for the prediction of radio-induced toxicity in prostate cancer patients. Calculations included dosimetric as well as clinical variables to help radiation oncologists predict late rectal morbidity, thus introducing the possibility of RT plan corrections to better tailor treatment to the patient's characteristics, to avoid unnecessary worsening of quality of life, and to provide support to the patient in selecting the best therapeutic approach.

A nomogram for predicting overall survival of women with endometrial cancer following primary therapy: toward improving individualized cancer care

OBJECTIVES

Traditionally we have relied mainly on final FIGO stage to estimate overall oncologic outcome in endometrial cancer patients. However, it is well known that other patient factors may play equally important roles in outcome. Our objective was to develop a clinically useful nomogram in the hope of providing a more individualized and accurate estimation of overall survival (OS) following primary therapy.

METHODS

Using a prospectively maintained endometrial cancer database, 1735 patients treated between 1993 and 2008 were analyzed. Characteristics known to predict OS were collected. For each patient, points were assigned to each of these 5 variables. A total score was calculated. The association between each predictor and the outcome was assessed by multivariable modeling. The corresponding 3-year OS probabilities were then determined from the nomogram.

RESULTS

The median age was 62 years (range, 25-96). Final grade included: G1 (471), G2 (622), G3 (634), and missing (8). Stage included: IA (501), IB (590), IC (141), IIA (36), IIB (75), IIIA (116), IIIB (6), IIIC (135), IVA (7), and IVB (128). Histology included: adenocarcinoma (1376), carcinosarcoma (100), clear cell (62), and serous (197). Median follow-up for survivors was 29.2 months (0-162.2 months). Concordance probability estimator for the nomogram is 0.746+/-0.011.

CONCLUSION

We developed a nomogram based on 5 easily available clinical characteristics to predict OS with a high concordance probability. This nomogram incorporates other individualized patient variables beyond FIGO stage to more accurately predict outcome. This new tool may be useful to clinicians in assessing patient risk when deciding on follow-up strategies.