New symptom-based predictive tool for survival at seven and thirty days developed by palliative home care teams

Decision tree algorithm to predict mortality in incurable cancer: a new prognostic model

OBJECTIVES

To develop and validate a new prognostic model to predict 90-day mortality in patients with incurable cancer.

METHODS

In this prospective cohort study, patients with incurable cancer receiving palliative care (n = 1322) were randomly divided into two groups: development (n = 926, 70%) and validation (n = 396, 30%). A decision tree algorithm was used to develop a prognostic model with clinical variables. The accuracy and applicability of the proposed model were assessed by the C-statistic, calibration and receiver operating characteristic (ROC) curve.

RESULTS

Albumin (75.2%), C reactive protein (CRP) (47.7%) and Karnofsky Performance Status (KPS) ≥50% (26.5%) were the variables that most contributed to the classification power of the prognostic model, named Simple decision Tree algorithm for predicting mortality in patients with Incurable Cancer (acromion STIC). This was used to identify three groups of increasing risk of 90-day mortality: STIC-1 - low risk (probability of death: 0.30): albumin ≥3.6 g/dL, CRP <7.8 mg/dL and KPS ≥50%; STIC-2 - medium risk (probability of death: 0.66 to 0.69): albumin ≥3.6 g/dL, CRP <7.8 mg/dL and KPS <50%, or albumin ≥3.6 g/dL and CRP ≥7.8 mg/dL; STIC-3 - high risk (probability of death: 0.79): albumin <3.6 g/dL. In the validation dataset, good accuracy (C-statistic ≥0.71), Hosmer-Lemeshow p=0.12 and area under the ROC curve=0.707 were found.

CONCLUSIONS

STIC is a valid, practical tool for stratifying patients with incurable cancer into three risk groups for 90-day mortality.

Machine learning applied to electronic health record data in home healthcare: A scoping review

OBJECTIVE

Despite recent calls for home healthcare (HHC) to integrate informatics, the application of machine learning in HHC is relatively unknown. Thus, this study aimed to synthesize and appraise the literature describing the application of machine learning to predict adverse outcomes (e.g., hospitalization, mortality) using electronic health record (EHR) data in the HHC setting. Our secondary aim was to evaluate the comprehensiveness of predictors used in the machine learning algorithms guided by the Biopsychosocial Model.

METHODS

During March 2022 we conducted a literature search in four databases: PubMed, Embase, CINAHL, and Scopus. Inclusion criteria were 1) describing services provided in the HHC setting, 2) applying machine learning algorithms to predict adverse outcomes, defined as outcomes related to patient deterioration, 3) using EHR data and 4) focusing on the adult population. Predictors were mapped to the Biopsychosocial Model. A risk of bias analysis was conducted using the Prediction Model Risk Of Bias Assessment Tool.

RESULTS

The final sample included 20 studies. Eighteen studies used predictors from standardized assessments integrated in the EHR. The most common outcome of interest was hospitalization (55%), followed by mortality (25%). Psychological predictors were frequently excluded (35%). Tree based algorithms were most frequently applied (75%). Most studies demonstrated high or unclear risk of bias (75%).

CONCLUSION

Future studies in HHC should consider incorporating machine learning algorithms into clinical decision support systems to identify patients at risk. Based on the Biopsychosocial model, psychological and interpersonal characteristics should be used along with biological characteristics to enhance risk prediction. To facilitate the widespread adoption of machine learning, stakeholders should encourage standardization in the HHC setting.

Prevalence of oral mucositis, dry mouth, and dysphagia in advanced cancer patients

BACKGROUND

Oral symptoms can be a sign of an underlying systemic condition and have a significant impact on quality of life, nutrition, and cost of care, while these lesions are often studied in the context of cancer treatment. However, information regarding oral symptoms in advanced cancer patients is poor. The aim of this multicenter study was to determine the prevalence and the characteristics of oral symptoms in a large population of advanced cancer patients.

METHODS

A consecutive sample of patients with advanced cancer for a period of 6 months was prospectively assessed for an observational study. At time of admission, the epidemiological characteristics, surgery-radiotherapy of head and neck, and oncologic treatments in the last month were recorded. The presence of mucositis, dry mouth, and dysphagia was assessed by clinical examination and patients' report and their intensity recorded. Patients were also asked whether they had limitation on nutrition of hydration due to the local condition.

RESULTS

Six hundred sixty-nine patients were surveyed in the period taken into consideration. The mean age was 72.1 years (SD 12.3), and 342 patients were males. The primary tumors are listed in Table 1. The prevalence of mucositis was 22.3 %. The symptom relevantly reduced the ingestion of food or fluids and was statistically associated with the Karnofsky level and head and neck cancer. The prevalence of dry mouth was 40.4 %, with a mean intensity of 5.4 (SD 2.1). Several drugs were concomitantly given, particularly opioids (78 %), corticosteroids (75.3 %), and diuretics (70.2 %). Various and nonhomogeneous treatments were given for dry mouth, that was statistically associated with current or recent chemotherapy, and hematological tumors. The prevalence of dysphagia was 15.4 % with a mean intensity of 5.34 (SD 3). Dysphagia for liquids was observed in 52.4 % of cases. A high level of limitation for oral nutrition due to dysphagia was found, and in 53.4 % of patients, alternative routes to the oral one were used. Dysphagia was statistically associated with the Karnofsky level and head and neck cancer. A strong relationship between the three oral symptoms was found.

CONCLUSION

In advanced cancer patients, a range of oral problems significantly may impact on the physical, social, and psychological well-being of advanced cancer patients to varying degrees. These symptoms should be carefully assessed early but become imperative in the palliative care setting when they produce relevant consequences that may be life-threatening other than limiting the daily activities, particularly eating and drinking.